Speech recognition is known in the art. In general, speech audio input is digitized and then processed to facilitate identification of specific spoken words contained in the speech input. Pursuant to one approach, so-called features are extracted from the digitized speech and then compared against previously stored patterns to enable such recognition of the speech content. It is also known to parse or distribute the feature extraction and pattern matching activities over two different processing units. For example, The European Technical Standards Institute (ETSI) has proposed, in Standard ES 201 108, Ver. 1.1.2, April 2000, a distributed speech recognition system where a portable device such as a cellular handset executes the feature extraction function and transmits the resultant features to a fixed-end platform that then facilitates the pattern matching function.
It is also known that, in general, pattern matching can be more successfully accomplished when the input can be accurately characterized as being either speech or non-speech audio input. For example, when information is available to identify a given segment of audio input as being non-speech, that information can be used to beneficially influence the functionality of the pattern matching activity by, for example, eliminating pattern matching for that particular non-speech segment. Unfortunately, the benefits of voice activity detection are not ordinarily available in distributed speech recognition systems such as the ETSI standard noted above. Instead, the speech content features are transmitted sans any voice activity detection information to the remote pattern matching platform.
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